CN104408392A - Class-based tag loss rate detection method - Google Patents
Class-based tag loss rate detection method Download PDFInfo
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- CN104408392A CN104408392A CN201410692934.6A CN201410692934A CN104408392A CN 104408392 A CN104408392 A CN 104408392A CN 201410692934 A CN201410692934 A CN 201410692934A CN 104408392 A CN104408392 A CN 104408392A
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Abstract
The invention discloses a class-based tag loss rate detection method. The method includes: firstly, on the basis of classes, classifying all tags in a scanning range of a reader according to class ID (identification); secondly, placing emphasis on loss rate, and macroscopically mastering loss conditions of each class instead of confining to scattered quantity; finally, instead of counting according to success replay of each turn, each frame and each time slot, using a small part of success time slot reply to estimate overall conditions until a result falls in a set confidence interval. Therefore, precision is guaranteed, time is greatly saved, efficiency is improved, and practical significance and high operability are achieved.
Description
Technical field
The present invention relates to technical field of RFID, particularly relate to a kind of label Loss Rate detection method based on classification.
Background technology
Radio-frequency (RF) identification (Radio Frequency Identification, RFID) technology is a kind of wireless communication technology, read and write related data by radio signals identification specific objective, and without the need to setting up machinery or optical contact between recognition system and specific objective.Many industries have all used REID: be such as attached to by label on an automobile produced, this car progress on a production line just can be followed the trail of by manufacturer; Label is attached on the medicine in warehouse, just can inquires about the surplus of medicine at any time, follow the trail of the position of medicine; Label is attached on livestock and pet, just actively can identifies livestock and pet; In like manner, in stock control can easily test item lose situation.In the embody rule such as stock control, material flow tracking, management of retail business, detect, identify that losing label is a class major issue.
Label Loss Rate problem, people are that indirect detection is lost, for a reader and scope interior label thereof, reader detects existing label and the contrast that should exist one by one and in theory, namely losing of vacancy.For tag recognition test problems, traditional treatment method is as follows: all labels within the scope of reader scans oneself, sends inquiry request to them, and label replys oneself identity recognition number (ID) after receiving request, self existence for confirmation.If multiple label is replied at one time, can clash, now reader just None-identified, according to said method, experiment proves, label on average sends 2.72 ID, could send successfully, be confirmed.Suppose there is N number of label within the scope of a reader, then confirm that the T.T. needed is 2.72N (t
tag+ t
s), t
tag=2.4ms, t
s=0.4ms, wherein t
sfor the tag reactant time.Raising the efficiency for saving time, the ID of tag return oneself should be avoided.Therefore propose new agreement TPP, it avoids each label to reply the ID of oneself, and the time of replying is divided into time slot one by one, adds random code salted hash Salted simultaneously, allows each label random Harsh numerical value, to reply at the time slot of this numerical value.This Hash procedure, reader understands self Hash one time, so its foreseen outcome, when tag return, just can contrast one by one: certain time slot should reply one, actually replys one, and namely this label is not lost, otherwise, lose; Certain time slot should reply two, does not but receive reply, then entirely lose for two; Certain time slot should reply one, but has two and plural tag return, then clashes.And label only need reply a bit information, reader just can confirm that whether it exists, and for the identical label of namely replying conflict of cryptographic hash, then remove the ID replying oneself, so reader confirms that within the scope of oneself, all label required times are (t
tag+ t
s) * N
1+ f*t
s, wherein N
1for the number of tags clashed, f is frame length and timeslot number.Relatively traditional all labels all will reply the method for ID, and these class methods only need reply ID for conflict label, and all the other only send a bit acknowledgement information, therefore have saved the time to a certain extent, improve efficiency.
If but a time slot only has two labels to clash, just need all to reply oneself ID, still very consuming time, TPP/TR solves this problem.TPP/TR agreement emphasis solves single time slot two labels and replys problem simultaneously, and it removes a label, allows it reply the ID of oneself, then replys a bit information for remaining one, and therefore, it decreases again the number of conflict label to a certain extent indirectly.In like manner, reply problem for three labels, TPP/CSTR agreement gives to solve, and it makes the ID of one of them tag return oneself simultaneously, remains two labels and provides long reply, i.e. t
l=0.8ms, also decreases the quantity of conflict to a certain extent indirectly.But, reply ID, i.e. required time t
tag=2.4ms, consuming time many, efficiency is low, IIP agreement thoroughly abandons the reply of ID, introduce Bloom filter (Bloom Filter), all labels all only send a bit acknowledgement information, add and take turns the method such as detection, probability selection reply more, largely improve efficiency, significantly save the time.
Said method something in common is to avoid conflict, and gives different solutions to conflict time slot; Also there is certain methods to utilize conflict in recent years, based on technology such as compressed sensing, be intended to raise the efficiency.But each class methods all do not consider label classification, be all carry out recognition detection after attempting to utilize each time slot to become successful single time slot, there is no grasp macroscopical, and unactual, be difficult to the problem in Coping with Reality, only rest on theory stage.Such as, in stock, wonder the Loss Rate of category-A article, want to allow some class Loss Rate report to the police to take measures more than 5% again, be less than 5% ignore, said method is all difficult to solve these practical problemss.
Summary of the invention
The object of the invention is to, by a kind of label Loss Rate detection method based on classification, solve the problem that above background technology part is mentioned.
For reaching this object, the present invention by the following technical solutions:
Based on a label Loss Rate detection method for classification, it comprises the steps:
A, according to category IDs, all labels in reader self sweep limit to be divided into groups;
B, based on grouping and classification, estimation overall method in local is utilized to detect number of missing, the Loss Rate of often organizing label, the number of missing often organizing label detected and predetermined threshold value are compared, if be less than predetermined threshold value, then this group dormancy, the reply often taken turns after not participating in, if be greater than predetermined threshold value, then enter next round to detect, until the precision of the label Loss Rate detected falls into default fiducial interval.
Especially, described steps A specifically comprises: reader sends request command to all labels in self sweep limit, and the parameter that this request command carries comprises Hash seed r and default packet count W, waits for tag return; Label according to oneself category IDs, Hash seed r, preset packet count W and carry out Hash, be hashing onto different groups, then carry out replys confirmation.
Especially, described default packet count W calculates after the experiment of interior correlation parameter according to total number of labels, label classification number.
Especially, in same grouping after the label Hash that in described steps A, category IDs is identical, and at least existence grouping comprises the label of at least two kinds.
Especially, also comprise before described steps A: background server or reader are added up the classification of all labels in reader self sweep limit situation of replying according to Hash seed r and category IDs, can predict due reply situation with reference to this statistics.
Especially, based on grouping and classification in described step B, utilize estimation overall method in local to detect number of missing, the Loss Rate of often organizing label, specifically comprise: comprise C2, C3 two class label for grouping to be detected, make n
s, 2represent the single number of time slot of C2 class tag return, n
sfor the single total number of timeslots order of C2 and C3 two class tag return, n
2represent the total number of C2 class label, n represents the sum of C2 and C3 two class label, after Hash, obtains C2, C3 two single time slot of success of class tag return, namely obtains n
s, 2, n
svalue, and the n theoretical value that to be reader known, then by formula
obtain n
2value, by n
2value and the original number of C2 class label compare, the difference drawn is the number of missing of C2 class label, and then ratio both calculating, and is the Loss Rate of C2 class label.
The label Loss Rate detection method based on classification that the present invention proposes, first based on classification, according to category IDs, is classified to all labels within the scope of reader scans; Secondly, lay particular emphasis on Loss Rate, from the loss situation macroscopically holding each class, and be not only confined to scattered quantity; Finally, add up not based on the successful reply often taking turns each time slot of every frame, but estimate overall condition with the reply of fraction success time slot, until result drops in the fiducial interval of setting, not only ensure that precision, significantly save the time, improve efficiency, and there is practical significance, workable.
Accompanying drawing explanation
The label Loss Rate detection method process flow diagram based on classification that Fig. 1 provides for the embodiment of the present invention;
The grouping situation schematic diagram of the label within the scope of the reader scans that Fig. 2 provides for the embodiment of the present invention;
The bar shaped schematic diagram of label number of missing in three groupings that Fig. 3 provides for the embodiment of the present invention.
Embodiment
Below in conjunction with drawings and Examples, the invention will be further described.Be understandable that, specific embodiment described herein is only for explaining the present invention, but not limitation of the invention.It also should be noted that, for convenience of description, illustrate only part related to the present invention in accompanying drawing but not full content, unless otherwise defined, all technology used herein and scientific terminology are identical with belonging to the implication that those skilled in the art of the present invention understand usually.The object of term used in the description of the invention herein just in order to describe specific embodiment, is not intended to be restriction the present invention.Term as used herein " and/or " comprise arbitrary and all combinations of one or more relevant Listed Items.
Please refer to shown in Fig. 1, the label Loss Rate detection method process flow diagram based on classification that Fig. 1 provides for the embodiment of the present invention.
Label Loss Rate detection method based on classification in the present embodiment specifically comprises the steps:
S101, according to category IDs, all labels in reader self sweep limit to be divided into groups.
Reader sends request command to all labels in self sweep limit, and the parameter that this request command carries comprises Hash seed r and default packet count W, waits for tag return; Label according to oneself category IDs, Hash seed r, preset packet count W and carry out Hash, be hashing onto different groups, then carry out replys confirmation.Other label of same class, category IDs is identical, can be hashing onto same grouping; Because classification number is greater than packet count, so at least existence grouping comprises the label of at least two kinds.Wherein, described default packet count W calculates after the experiment of interior correlation parameter according to total number of labels, label classification number, and its value ensure that time optimal.It should be noted that, in order to predict due reply situation, background server or reader are added up the classification of all labels in reader self sweep limit situation of replying according to Hash seed r and category IDs in advance, can predict due reply situation with reference to this statistics.As shown in Figure 2, there is C1 to C7 seven class label in self sweep limit of reader, seven class labels are hashing onto three groups according to the category IDs of oneself.In Group1 that is first group, contain C5, C6, C7 tri-class label, in Group2 that is second group, contain C2, C3 two class label, in Group3 that is the 3rd group, contain C1, C4 two class label.Wherein, preset packet count W be 3 test according to actual conditions after draw; Classification number 3 in first group is that label draws after also replying according to Hash seed r, category IDs, default packet count W acting in conjunction.
S102, based on grouping and classification, estimation overall method in local is utilized to detect number of missing, the Loss Rate of often organizing label, the number of missing often organizing label detected and predetermined threshold value are compared, if be less than predetermined threshold value, then this group dormancy, the reply often taken turns after not participating in, if be greater than predetermined threshold value, then enter next round to detect, until the precision of the label Loss Rate detected falls into default fiducial interval.
As shown in Figure 3,301 is the number of missing bar chart of label in first group, and 302 is the number of missing bar chart of label in second group, and 303 is the number of missing bar chart of label in the 3rd group, and λ is predetermined threshold value, and M is the loss number of label.In first group, the number of missing of label is less than predetermined threshold value, this group dormancy, the reply often taken turns after not participating in.In second group and the 3rd group, the number of missing of label is greater than predetermined threshold value, and emphasis detects.Utilize estimation overall method in local to detect number of missing, the Loss Rate of often organizing label, specifically comprise: for second group, make n
s, 2represent the single number of time slot of C2 class tag return, n
sfor the single total number of timeslots order of C2 and C3 two class tag return, n
2represent the total number of C2 class label, n represents the sum of C2 and C3 two class label, after Hash, obtains C2, C3 two single time slot of success of class tag return, namely obtains n
s, 2, n
svalue, and the n theoretical value that to be reader known, then by formula
obtain n
2value, by n
2value and the original number of C2 class label compare, the difference drawn is the number of missing of C2 class label, and then ratio both calculating, and is the Loss Rate of C2 class label.For the testing result of often taking turns, its precision should fall into default fiducial interval, if do not meet accuracy requirement, then enters next round and detects, until the precision of the label Loss Rate detected falls into default fiducial interval.For final result, grouping situation can also be upset, remove the grouping of dormancy, again detect.
Technical scheme of the present invention applies in label loss problem first, avoids the complex process of each time slot of recognition detection in classic method.The present invention have ignored conflict time slot, simplicity of design, and the error caused thus solves by arranging fiducial interval.Experiment proves, the present invention effectively can detect the Loss Rate of all kinds of article, and efficiency is high, and practical significance is great.Compared with conventional labels Loss Rate detection method, advantage of the present invention is as follows: the problem one, solved is the problem in real life, is not limited to theoretic Construct question.Two, based on grouping and classification, the detection of Loss Rate, from macroscopically considering loss situation, and is not limited to the loss detecting some labels emphatically.Three, adopt local to estimate entirety, get successfully single number of time slot and estimate, avoid traditional collision problem, introduce fiducial interval and ensure that precision.
Note, above are only preferred embodiment of the present invention and institute's application technology principle.Skilled person in the art will appreciate that and the invention is not restricted to specific embodiment described here, various obvious change can be carried out for a person skilled in the art, readjust and substitute and can not protection scope of the present invention be departed from.Therefore, although be described in further detail invention has been by above embodiment, the present invention is not limited only to above embodiment, when not departing from the present invention's design, can also comprise other Equivalent embodiments more, and scope of the present invention is determined by appended right.
Claims (6)
1., based on a label Loss Rate detection method for classification, it is characterized in that, comprise the steps:
A, according to category IDs, all labels in reader self sweep limit to be divided into groups;
B, based on grouping and classification, estimation overall method in local is utilized to detect number of missing, the Loss Rate of often organizing label, the number of missing often organizing label detected and predetermined threshold value are compared, if be less than predetermined threshold value, then this group dormancy, the reply often taken turns after not participating in, if be greater than predetermined threshold value, then enter next round to detect, until the precision of the label Loss Rate detected falls into default fiducial interval.
2. the label Loss Rate detection method based on classification according to claim 1, it is characterized in that, described steps A specifically comprises: reader sends request command to all labels in self sweep limit, the parameter that this request command carries comprises Hash seed r and default packet count W, waits for tag return; Label according to oneself category IDs, Hash seed r, preset packet count W and carry out Hash, be hashing onto different groups, then carry out replys confirmation.
3. the label Loss Rate detection method based on classification according to claim 2, is characterized in that, described default packet count W calculates after the experiment of interior correlation parameter according to total number of labels, label classification number.
4. the label Loss Rate detection method based on classification according to claim 2, is characterized in that, in same grouping after the label Hash that in described steps A, category IDs is identical, and at least existence grouping comprises the label of at least two kinds.
5. the label Loss Rate detection method based on classification according to claim 1, it is characterized in that, also comprise before described steps A: background server or reader are added up the classification of all labels in reader self sweep limit situation of replying according to Hash seed r and category IDs, can predict due reply situation with reference to this statistics.
6. the label Loss Rate detection method based on classification according to claim 1, it is characterized in that, based on grouping and classification in described step B, estimation overall method in local is utilized to detect number of missing, the Loss Rate of often organizing label, specifically comprise: comprise C2, C3 two class label for grouping to be detected, make n
s, 2represent the single number of time slot of C2 class tag return, n
sfor the single total number of timeslots order of C2 and C3 two class tag return, n
2represent the total number of C2 class label, n represents the sum of C2 and C3 two class label, after Hash, obtains C2, C3 two single time slot of success of class tag return, namely obtains n
s, 2, n
svalue, and the n theoretical value that to be reader known, then by formula
obtain n
2value, by n
2value and the original number of C2 class label compare, the difference drawn is the number of missing of C2 class label, and then ratio both calculating, and is the Loss Rate of C2 class label.
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| CN103268465B (en) * | 2013-06-08 | 2016-08-10 | 无锡儒安科技有限公司 | The method for quickly identifying of label classification in a kind of radio-frequency recognition system |
| CN103761494B (en) * | 2014-01-10 | 2017-02-01 | 清华大学 | Method and system for identifying lost tag of RFID system |
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